Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	New York	20,966
2	New Jersey	20,024
3	Louisiana	17,671
4	Arizona	17,602
5	Rhode Island	16,602
6	Massachusetts	16,268
7	District of Columbia	15,509
8	Florida	13,577
9	Connecticut	13,331
10	Delaware	13,318
11	Mississippi	12,614
12	Illinois	12,394
13	Maryland	12,378
14	South Carolina	11,728
15	Alabama	11,677
16	Iowa	11,391
17	Nebraska	11,226
18	Georgia	10,791
19	Arkansas	9,852
20	Texas	9,816
21	Nevada	9,632
22	Utah	9,599
23	Tennessee	9,578
24	California	8,771
25	North Carolina	8,568
26	South Dakota	8,559
27	Virginia	8,487
28	Indiana	8,021
29	Pennsylvania	7,917
30	Michigan	7,814
31	Minnesota	7,661
32	New Mexico	7,398
33	Wisconsin	7,130
34	Kansas	6,984
35	Idaho	6,693
36	Colorado	6,557
37	North Dakota	5,901
38	Ohio	5,816
39	Washington	5,811
40	Oklahoma	5,493
41	Missouri	5,021
42	Kentucky	4,633
43	New Hampshire	4,479
44	Wyoming	3,371
45	Puerto Rico	3,169
46	Oregon	3,037
47	Maine	2,652
48	Alaska	2,593
49	West Virginia	2,459
50	Vermont	2,091
51	Montana	1,906
52	Hawaii	877

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Florida	576
2	Louisiana	375
3	Arizona	372
4	South Carolina	368
5	Alabama	360
6	Texas	303
7	Nevada	298
8	Tennessee	265
9	Idaho	257
10	Georgia	251
11	Mississippi	237
12	California	224
13	Arkansas	206
14	North Carolina	183
15	Utah	177
16	Oklahoma	165
17	Kansas	156
18	Wisconsin	138
19	Iowa	131
20	Washington	128
21	Missouri	117
22	Nebraska	117
23	New Mexico	117
24	Montana	112
25	Alaska	109
26	North Dakota	109
27	Ohio	107
28	Virginia	103
29	Maryland	97
30	Minnesota	94
31	Kentucky	87
32	Rhode Island	86
33	Indiana	81
34	Puerto Rico	79
35	Delaware	77
36	Oregon	75
37	District of Columbia	68
38	Illinois	66
39	Colorado	65
40	Wyoming	64
41	Pennsylvania	54
42	Michigan	51
43	West Virginia	48
44	South Dakota	44
45	New York	36
46	Massachusetts	35
47	New Jersey	35
48	Connecticut	22
49	New Hampshire	16
50	Hawaii	14
51	Maine	11
52	Vermont	11

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,754
2	New York	1,649
3	Connecticut	1,226
4	Massachusetts	1,210
5	Rhode Island	929
6	District of Columbia	804
7	Louisiana	741
8	Michigan	633
9	Illinois	585
10	Maryland	551
11	Pennsylvania	544
12	Delaware	531
13	Mississippi	427
14	Indiana	412
15	Arizona	321
16	Colorado	301
17	New Hampshire	288
18	Georgia	282
19	Minnesota	274
20	New Mexico	262
21	Ohio	262
22	Iowa	240
23	Alabama	237
24	Virginia	231
25	Florida	205
26	Nevada	199
27	South Carolina	192
28	Washington	192
29	Missouri	185
30	California	182
31	Nebraska	154
32	North Carolina	150
33	Kentucky	148
34	Wisconsin	143
35	South Dakota	123
36	North Dakota	120
37	Texas	118
38	Tennessee	110
39	Arkansas	109
40	Oklahoma	108
41	Kansas	104
42	Vermont	89
43	Maine	84
44	Utah	70
45	Oregon	58
46	Idaho	57
47	West Virginia	54
48	Puerto Rico	52
49	Wyoming	38
50	Montana	31
51	Alaska	20
52	Hawaii	14

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Arizona	8
2	Mississippi	4
3	Alabama	3
4	Florida	3
5	Louisiana	3
6	Connecticut	2
7	Nevada	2
8	New Jersey	2
9	Rhode Island	2
10	South Carolina	2
11	Texas	2
12	Arkansas	1
13	California	1
14	Georgia	1
15	Illinois	1
16	Iowa	1
17	Kansas	1
18	Kentucky	1
19	Maryland	1
20	Massachusetts	1
21	Missouri	1
22	Montana	1
23	New Mexico	1
24	New York	1
25	North Carolina	1
26	Tennessee	1
27	Utah	1
28	Washington	1
29	Alaska	0
30	Colorado	0
31	Delaware	0
32	District of Columbia	0
33	Hawaii	0
34	Idaho	0
35	Indiana	0
36	Maine	0
37	Michigan	0
38	Minnesota	0
39	Nebraska	0
40	New Hampshire	0
41	North Dakota	0
42	Ohio	0
43	Oklahoma	0
44	Oregon	0
45	Pennsylvania	0
46	Puerto Rico	0
47	South Dakota	0
48	Vermont	0
49	Virginia	0
50	West Virginia	0
51	Wisconsin	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Trousdale	Tennessee	134,084	1	99
Lake	Tennessee	99,202	2	99
Lee	Arkansas	95,969	3	99
Dakota	Nebraska	92,829	4	99
Buena Vista	Iowa	88,583	5	99
Davidson	Tennessee	22,291	123	96
Richland	South Carolina	12,577	425	86
Orange	California	8,225	786	74
York	South Carolina	7,079	921	70
Pierce	Washington	4,088	1516	51

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Hancock	Georgia	3,902	1	99
Randolph	Georgia	3,688	2	99
Terrell	Georgia	3,282	3	99
Early	Georgia	3,042	4	99
Emporia city	Virginia	2,806	5	99
Richland	South Carolina	238	676	78
Davidson	Tennessee	223	709	77
Orange	California	136	992	68
Pierce	Washington	126	1035	67
York	South Carolina	46	1654	47

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.